Dr. Clare Fitzpatrick joined Boise State University in August 2016 as an Assistant Professor of Mechanical and Biomedical Engineering and director of the Computational Biosciences Lab. The overall objective of Dr. Fitzpatrick’s work is to directly impact patient quality of life through guiding surgical decisions and developing and evaluating implants and surgical techniques which optimize patient functionality and mobility. She works primarily in the area of computational biomechanics, with a focus on finite element modeling.

Dr. Fitzpatrick was awarded her Ph.D. in Mechanical Engineering in 2008 from University College Dublin, Ireland. This work forced on development of statistical shape models of the knee joint, and application of these models to optimize implant sizing of knee replacement devices. Prior to joining Boise State, Dr. Fitzpatrick worked as a Senior Research Engineer at the University of Denver’s Center for Orthopaedic Biomechanics. Her work focused on computational finite element modeling with applications in orthopaedic biomechanics, and involved extensive collaboration with academic, industry and clinical partners.

Education

B.E., Mechanical Engineering, May 2003, University College Dublin, Ireland

Ph.D., Mechanical Engineering, March 2008, University College Dublin, Ireland

Rullkoetter PJ, Fitzpatrick CK, and Clary CW, 2017. “How can we use computational modeling to improve TKA? Modeling stability and mobility in the implanted knee”. Journal of American Academy of Orthopaedic Surgeons, 25, S33-S39.

Fitzpatrick CK, FitzPatrick DP, and Auger DD, 2008. “Size and shape of the resection surface geometry of the osteoarthritic knee in relation to total knee replacement design”. Proceedings from the Institute of Mechanical Engineers Part H 222, 923-932.

Understanding the mechanisms of injury and disease in order to design therapeutic and surgical interventions which are tailored to correct these mechanistic deficiencies. Oftentimes, these biomechanical issues are difficult to elucidate. Computational models can be used to address specific clinical issues, notably, recurrent patellar dislocation in the intact knee, and patellofemoral crepitation in the implanted knee.

Accounting for uncertainty in loading conditions and surgical process in the patient population through application of statistical and probabilistic methods. There is a large amount of uncertainty in loading conditions and surgical process within the joint replacement patient population. Accuracy of surgical placement of components, patient weight, activity level and soft-tissue integrity are just a sub-set of parameters which contribute to differences in clinical outcomes between patients. As a result, differentiating between the performance of different components is a difficult question to address. Statistical and probabilistic methods have been developed as part of Dr. Fitzpatrick’s work which integrate with the computational environment in order to design implants which are robust to patient and surgical variability. These probabilistic tools have been used to identify the factors which have the greater influence on joint mechanics, and statistical methods to predict joint mechanics based on quantification of patient anatomy.

Developing computational testbeds for pre-clinical testing and design-phase evaluation of implant devices such as knee and hip replacements in order to evaluate the performance of these devices in vivo. Traditional pre-clinical testing of implanted devices such as knee and hip replacement implants comprises of basic evaluation in an attempt to evaluate their performance in vivo

loading conditions are simplistic and do not accuracy represent the loads encountered within the population. Dr. Fitzpatrick has developed sophisticated computational test-beds for pre-clinical testing and design-phase evaluation of components in order to provide accurate predictions of in vivo performance for prospective implant designs. These models integrate feedback control systems with the finite element environment to accurately reproduce the conditions of activities of daily activities.